import numpy as np
from sklearn.metrics import accuracy_score


class LogisticRegression:

	def __init__(self):
		self.coef_ = None
		self.intercept_ = None
		self._theta = None

	def _sigmoid(self, t):
		return 1. / (1. + np.exp(-t))

	def fit(self, X_train, y_train, eta=0.01, n_iters=1e4):
		assert X_train.shape[0] == y_train.shape[0], "size must be same"

		def J(theta, X_b, y):
			y_hat = self._sigmoid(X_b.dot(theta))
			try:
				return - np.sum(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat)) / len(X_b)
			except:
				return float('inf')

		def dJ(theta, X_b, y):
			return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) / len(y)

		def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
			theta = initial_theta
			cur_iter = 0

			while cur_iter < n_iters:
				gradient = dJ(theta, X_b, y)
				last_theta = theta
				theta = theta - eta * gradient

				if abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon:
					break

				cur_iter += 1

			return theta

		X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
		initial_theta = np.zeros(X_b.shape[1])

		self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)

		self.coef_ = self._theta[1:]
		self.intercept_ = self._theta[0]

		return self

	def predict_proba(self, X_test):
		assert self.coef_ is not None and self.intercept_ is not None, "must fit before predict"
		assert X_test.shape[1] == len(self.coef_), "feature number must be same"

		X_b = np.hstack([np.ones((X_test.shape[0], 1)), X_test])
		return self._sigmoid(X_b.dot(self._theta))

	def predict(self, X_test):
		assert self.coef_ is not None and self.intercept_ is not None, "must fit before predict"
		assert X_test.shape[1] == len(self.coef_), "feature number must be same"

		proba = self.predict_proba(X_test)
		return np.array(proba >= 0.5, dtype='int')

	def score(self, X_test, y_test):
		assert X_test.shape[0] == len(y_test), "length must be same"

		y_predict = self.predict(X_test)
		return accuracy_score(y_test, y_predict)

	def __repr__(self):
		return "LogisticRegression()"
